Anomaly detection in multivariate time series is a data mining task with applications to information technology (IT) ecosystem modeling, network traffic monitoring, medical diagnosis: and other domains. Anomaly detection is the identification of items, events or observations that do not conform to an expected pattern in a dataset. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions. Typically, the anomalous items translate to a particular problem or incident within an application. For example, a detected anomaly may identify an incident with a particular server or component in an IT ecosystem, which may result in operational issues that impact production.